SViT: Revisiting Token Pruning for Object Detection and Instance Segmentation

This repository contains code for the paper "Revisiting Token Pruning for Object Detection and Instance Segmentation ".

Abstract

Vision Transformers (ViTs) have shown impressive performance in computer vision, but their high computational cost, quadratic in the number of tokens, limits their adoption in computation-constrained applications. However, this large number of tokens may not be necessary, as not all tokens are equally important. In this paper, we investigate token pruning to accelerate inference for object detection and instance segmentation, extending prior works from image classification. Through extensive experiments, we offer four insights for dense tasks: (i) tokens should not be completely pruned and discarded, but rather preserved in the feature maps for later use. (ii) reactivating previously pruned tokens can further enhance model performance. (iii) a dynamic pruning rate based on images is better than a fixed pruning rate. (iv) a lightweight, 2-layer MLP can effectively prune tokens, achieving accuracy comparable with complex gating networks with a simpler design. We evaluate the impact of these design choices on COCO dataset and present a method integrating these insights that outperforms prior art token pruning models, significantly reducing performance drop from ~1.5 mAP to ~0.3 mAP for both boxes and masks. Compared to the dense counterpart that uses all tokens, our method achieves up to 34% faster inference speed for the whole network and 46% for the backbone.

Preparation

recommended environment:

pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html
pip install timm==0.4.12
pip install mmdet==2.28.1
pip install scipy
cd ops & sh make.sh # compile deformable attention

Data Preparation

Please prepare COCO according to the guidelines in MMDetection.
Alternatively, download download_dataset.py and run python download_dataset.py --dataset-name coco2017.
The dataset should have (or symlinked to have) the following folder structure:

root_folder
├── mmcv_custom
├── mmdet_custom
├── configs
├── ops
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017

Results

The following models exploit Mask R-CNN and use ViT-Adapter as backbones, which adapt DeiT without windows in this repo, since token pruning is incompatible with windows. Token pruning is introduced in the fintuing after the dense model has been trained for 36 epochs, and the finetuning contains 4 or 6 epochs.

Backbone	Pre-train	Lr schd	box AP	mask AP	#Param	FPS	Config	Download	Logs
ViT-Adapter-T	DeiT-T	3x+MS	45.8	40.9	28M	18.45	config	model	log
SViT-Adapter-T	ViT-Adapter-T	0.5x+MS	45.5	40.7	28M	22.32	config	model	log
ViT-Adapter-S	DeiT-S	3x+MS	48.5	42.8	48M	11.70	config	model	log
SViT-Adapter-S	ViT-Adapter-S	0.33x+MS	48.2	42.5	48M	15.75	config	model	log

Evaluation

To evaluate SViT-Adapter-S on COCO val2017 on a single node with 8 gpus run:

sh dist_test.sh configs/mask_rcnn/svit-adapter-s-0.33x-ftune.py pretrained/svit-adapter-s-0.33x.pth 8 --eval bbox segm

Training

Dense Training:

To train a dense ViT-Adapter-T with global attention (Mask R-CNN) on COCO train2017 on a single node with 4 gpus for 36 epochs run:

sh dist_train.sh configs/mask_rcnn/vit-adapter-t-3x.py 4

To train a dense ViT-Adapter-S with global attention (Mask R-CNN) on COCO train2017 on a single node with 8 gpus for 36 epochs run:

sh dist_train.sh configs/mask_rcnn/vit-adapter-s-3x.py 8

The number of gpus x samples_per_gpu from the config file should be equal to 16.

Sparse Finetuning:

To finetune the sparse SViT-Adapter-T with pruned tokens (Mask R-CNN) on COCO train2017 on a single node with 4 gpus for 6 epochs run:

sh dist_train.sh configs/mask_rcnn/svit-adapter-t-0.5x-ftune.py 4

To finetune the sparse SViT-Adapter-S with pruned tokens (Mask R-CNN) on COCO train2017 on a single node with 8 gpus for 4 epochs run:

sh dist_train.sh configs/mask_rcnn/svit-adapter-s-0.33x-ftune.py 8

The number of gpus x samples_per_gpu from the config file should be equal to 16.

Speed Measurement

We provide the script to compare the models' speeds:

python speed_test.py

Image Demo

We provide the script to visualize the token pruning process:

python seletor_demo.py data/coco/val2017/000000046252.jpg configs/mask_rcnn/demo-svit-adapter-s-0.33x-ftune.py pretrained/svit-adapter-s-0.33x.pth

Citation

If this work is helpful for your research, please consider citing the following BibTex entry:

@InProceedings{Liu_2024_WACV,
  title={Revisiting Token Pruning for Object Detection and Instance Segmentation},
  author={Liu, Yifei and Gehrig, Mathias and Messikommer, Nico and Cannici, Marco and Scaramuzza, Davide},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2024}
}

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Code Acknowledgments

This project has used code from the following projects:

timm, DeiT, EViT, MMDetection and ViT-Adapter.